1

Faculty of Business and Law

Happy moves?

Assessing the impact of subjective well-being

on the emigration decision

Artjoms Ivlevs

Department of Accounting, Economics and Finance,

University of the West of England, Bristol, UK

Economics Working Paper Series 1402

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Happy moves?

Assessing the impact of life satisfaction on emigration intentions

Artjoms Ivlevs

Bristol Business School, University of the West of England1

Abstract

Recent literature suggests that higher levels of subjective well-being lead to greater work

productivity, better physical health and enhanced social skills. Because of these positive

externalities, policymakers across the world should be interested in attracting and retaining

happy and life-satisfied migrants. This paper studies whether higher levels of life satisfaction

contribute to one’s desire to move abroad. Using survey data from 30 countries of Eastern

Europe and Central Asia in instrumental variable analysis, I find that higher levels of life

satisfaction have a positive effect on the probability of reporting intentions and willingness to

migrate. This finding raises concerns about possible “happiness drain” in migrant-sending

countries and questions the usefulness of happiness-enhancing policies.

Keywords: Subjective well-being, life satisfaction, emigration, transition economies.

JEL: F22, O15, P2

1 Department of Accounting, Economics and Finance, Bristol Business School, University of the West of

England, Bristol BS16 1QY, UK. Tel: +44 117 32 83943, Fax:+44 117 32 82289, E-mail: a.ivlevs@uwe.ac.uk

Ivlevs is also affiliated with the Nottingham School of Economics, Aix-Marseille School of Economics and the

University of Latvia.

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“At the heart of every man there is an instinct of prosperity and greater

happiness. Our [Slovenian] emigrants, who are leaving for larger cities, either

in Germany or America, are not pale, draining, hungry and desperate, but are

strong, blooming, young men who are full of life and strength; and there are

the most vigorous women. On the sad road out of their homeland they are not

accompanied so much by despair but rather by expectations and the

awareness of their own forces and strength.”2

I. INTRODUCTION

The effects of migration on the well-being of receiving populations, migrants themselves and

their family members back home have for a long time been a question of primary interest for

academics and policy makers. A large and well-established literature has concentrated on the

monetary and other clearly quantifiable impacts of migration, seeking to answer, for example,

whether migration reduces wages of native workers. A more recent strand in migration

literature has started to explore the links between migration and subjective well-being.3

Migrant has been at its centre-stage, and the main question asked has been whether migration

experience makes people happier (Bartram, 2011; Bartram, 2013; De Jong et al., 2002; Senik,

2011; Stillman et al., in press). Closely related to this question has been an interrogation of

whether, in a source country, it is the happier people who are more likely to migrate, i.e.

whether migrants positively or negatively select on the basis of subjective well-being (Cai et

al., 2014; Chindarkar 2014; Otrashchenko and Popova, 2014; Bartram, 2013; Graham and

Markowitz, 2011).

There are several reasons why it is important to understand the interplay between migration

and subjective well-being. First, local and national governments across the world have been

increasingly considering and adopting happiness and life satisfaction as key policy variables

capturing individual welfare and societal progress (OECD, 2013; Office for National

Statistics, 2013; Helliwell et al., 2013; Diener et al., 2009). In this regard, policymakers in

receiving countries may be willing to know not only how migration affects natives’ wages

and employment prospects, but also whether migration makes local people less (or more)

2 A description of migration from Slovenia in the beginning of 20

th century, provided in 1906 by a Slovenian

politician Anton Korošec (Drnovsec, 2009: 61). 3 See Simpson (2013) and International Organisation for Migration (2013) for an overview of this literature.

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happy. Similarly, governments of migrant-sending countries may want to know how

emigration and remittances affect those left behind.4

Second, it has been shown that higher levels of individual subjective well-being result in

greater productivity (Oswald et al., 2012), creativity (Amabile et al., 2005; George and Zhou,

2007), income (De Neve and Oswald, 2012), physical health (Diener and Chan, 2011),

sociability, quality of social relationships, social capital, and social behaviour (De Neve et al.,

2013; Guven, 2011).5 Therefore, if a country receives immigrants, it should be interested in

getting a high proportion of happy people among them. Happier and, hence, more

productive, healthy and sociable migrants will, arguably, put less pressure on the welfare

state and integrate more successfully into the host society. However, the flip side of any

“happiness gain” for the migrant-receiving countries is “happiness drain” for the migrant-

sending countries. The policymakers of the latter should be concerned about the outflow of

happy people, as this may deprive them of the many positive externalities that happiness is

associated with. Thus, similarly to human capital, happiness is a valuable resource that both

migrant-receiving and sending countries may wish to acquire or retain, and a central question

that policymakers may wish to ask is whether it is the most or the least happy people who are

more prone to migration.

A burgeoning empirical literature has started to address this question. Several micro-level

studies suggest that there is a negative association between subjective well-being and

intentions/ willingness to migrate. Otrashchenko and Popova (2014) use the Eurobarometer

data to show that, in Central and Eastern Europe, people less satisfied with life are more

likely to report intentions to migrate – both internationally and domestically. Graham and

Markowitz (2011) and Chindarkar (2014) find that, in Latin America, subjective well-being

and intentions to migrate are negatively correlated. Cai et al. (2014), using Gallup World

Survey data for 116 countries, uncover a negative association between life satisfaction and

desire to migrate internationally. A more mixed picture is obtained by Polgreen and Simpson

(2011), who study the life satisfaction-emigration relationship at country level and find that in

relatively unhappy countries emigration rates fall as average country happiness increases,

4 The effects of migration of the subjective well-being of people in receiving and sending countries are studied

by Akay et al. (2014), Betz and Simpson (2013) and Borraz et al. (2008). 5 See De Neve et al. (2013) for an overview of the effects of subjective well-being on objective outcomes.

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while the opposite is true for the relatively happy countries; the highest emigration rates are,

thus, observed in the most and the least happy countries.

While the above mentioned studies provide useful insights on the emigration intentions -

subjective well-being nexus, they remain correlational studies and tell us little about causal

effects of happiness on emigration decision. In particular, endogeneity problems may arise if

preparation for migration temporarily renders people more or less happy relative to the

underlying, long-term levels of happiness (reverse causality) or if unobserved individual

characteristics drive both happiness and intentions to migrate. For example, Nowok et al.

(2013) study the evolution of the UK internal migrants’ happiness before and after migration

and find that migrants experience a significant fall in happiness just before the move;

happiness, however, returns to initial levels when the move takes place and remains at these

levels thereafter. Such migration-induced temporary drop in happiness supports the set-point

theory of subjective well-being, which posits that people have stable underlying levels (set-

points) of happiness, transitory deviations from which occur in the face of major life events

(marriage, unemployment, illness).6 While both internal and international migration can well

be events which have a temporary effect on migrants’ underlying happiness, the question still

remains whether it is the happier people (as measured by the stable long-term subjective well-

being levels) who are more prone to migration, and how policy-driven increases in individual

happiness would affect the propensity to migrate.

At a theoretical level, one could provide arguments in favour of both a negative and positive

selection into migration on the basis of subjective well-being. First, one can easily integrate

happiness into the (economists’ preferred) Neoclassical approach to emigration decision.

Assuming that 1) utility is represented by happiness rather than income or consumption and

2) people believe that the attainable happiness levels abroad are on average higher than those

at home,7 the Neoclassical model would predict that it is the least happy who will be more

likely to migrate because they have the most to get from migration. However, as a certain

amount of income is necessary to overcome migration costs meaning that migrants are not

always drawn from the bottom of the income distribution, we can hypothesise that a certain

6 See Headey (2010) for an overview of the literature on the set-point theory of subjective well-being.

7 It should be noted that this assumption is in conflict with the set-point theory of subjective well-being, which

posits that individual happiness remains unchanged in the long-run. There is no guarantee, however, that

prospective migrants are familiar with the set-point theory or that the theory holds in all life situations (Headey,

2010); prospective migrants may therefore continue to believe, rightly or wrongly, that migration would result in

happier lives.

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degree of happiness is required to overcome the psychological barriers to migration. One has

to be open-minded, optimistic and risk-loving in order to cross borders and live abroad; many

of these qualities will be positively correlated with subjective well-being. This argument is

supported by Ek et al. (2008) who find that rural-to-urban migrants in Finland are more

optimistic and satisfied with life than the non-migrants. Polgreen and Simpson (2011) also

argue that international migrants tend to be more optimistic, which could explain their finding

that, in relatively happy countries, emigration rates increase with average happiness. Thus, it

is not unreasonable to expect migrants to be positively selected on the basis of subjective

well-being – contrary to the prediction of the happiness-enhanced Neoclassical model. Which

of the alternative lines of reasoning fits the reality better is an empirical question, which has

not yet been fully answered in the literature.

To fill this gap, I undertake an empirical analysis of the subjective well-being and emigration

decision nexus. The data come from the “Life in Transition 2” survey (LITS-2), administered

by the EBRD and the World Bank in autumn 2010 in twenty nine post-socialist economies of

Central and Eastern Europe and Central Asia. First, I explore the relationship between life

satisfaction and emigration intentions in a ‘naïve’ regression analysis, paying particular

attention to possible non-linearities in that relationship, and find a strong evidence for a U-

shaped association between the two phenomena: the greatest intentions and willingness to

move abroad are reported by people who (at the moment of the interview) are the most and

the least satisfied with their lives. However, the substantive significance of this relationship is

low and such result may suffer from endogeneity due to reverse causality or unobserved

variables. Therefore, the second, and principal, objective of this study is to determine the

causal effects of subjective well-being on emigration decision. This is important as many

governments across the world are explicitly aiming at increasing their citizens’ levels of

subjective well-being. If it turns out that greater happiness leads to a greater desire to

emigrate, the usefulness of the happiness-enhancing policies may be questionable.

To identify the causal effects of subjective well-being on emigration intentions, I employ

instrumental variable analysis. Ideally, I want the instruments to predict long-term,

underlying component of one’s subjective well-being and have no independent effect on the

desire to emigrate. I hypothesise that such long-tern trends in happiness can be traced back to

respondents’ childhood environments and instrument subjective well-being with (1) parental

education and (2) the fact that someone in the respondent’s family was injured or killed

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during the World War II. The instruments appear appropriate, and the instrumental variable

analysis suggests that life satisfaction has a positive effect on both willingness and intentions

to migrate. Uncovering causal effects of subjective well-being on the desire to emigrate is the

main contribution of this study to the existing literature, which so far has concentrated on

(conditional) correlations between the two phenomena.

The empirical analysis of this paper is based on the use of life satisfaction to capture

subjective well-being, and emigration intentions to capture emigration decision. Both

measures can be subject to criticism. First, unlike the objective, clearly quantifiable metrics

of most variables used in economics, life satisfaction and happiness are self-reported and

subjective constructs. They can be understood differently by similar people within and

between countries, which makes it potentially difficult to interpret interpersonal comparisons

of happiness indices (Di Tella and MacCulloch, 2006). However, the subjective measures of

well-being have been extensively validated via psychological and brain-scan research, and

shown to be reliable, consistent (across time and space) and comparable measures of

individual well-being (Frey and Stutzer, 2002; Layard, 2005; Easterlin, 2001; Polgreen and

Simpson, 2011; Diener et al., 2012). Given the increasing importance of happiness for policy,

there has recently been an explosion in research on subjective well-being among the

economists (see, e.g., MacKerron (2012) for an overview), who have been more willing to

accept happiness as a manifestation of utility and well-being.

A final note of caution concerns the use of emigration intentions data as a proxy for actual

emigration. Although it is not without criticism – there is no guarantee that intentions will be

followed by an actual move – longitudinal studies have shown that emigration intentions are

a good predictor of actual future emigration.8 Creighton (2013) showed that, in Mexico,

aspirations to move internally and internationally explain the subsequent moves. Van Dalen

and Henkens (2013) found that one-third of native Dutch residents who had stated an

intention to move abroad actually emigrated within the following five years. Böheim and

Taylor (2002) showed that the propensity to move internally in the UK is three times higher

for those who earlier expressed a preference to move than those who did not. At a theoretical

level, De Jong (1999, 2000) argues that the intentions to move are the primary determinant of

the migration behaviour and Burda et al. (1998) posit that intentions are a monotonic function

8 See, e.g., Friebel et al. (2013), Ivlevs (2013), Ivlevs and King (2012), and references therein, for recent

empirical studies using emigration intentions data to determine the profile of future migrants.

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of the variables which motivate migration. A specific advantage of using migration intentions

data is that it avoids the sample selection issues that arise from the use of the data on actual

immigrants collected by host countries, for instance, when immigration policies are designed

to attract more (or less) skilled migrants (Liebig and Sousa-Poza, 2004).9 It should also be

remembered that the migrant – once migrated – can no longer be observed in the source

country. Tracking down each migrant after the arrival in the host country and obtaining large

representative samples of immigrants would be very costly; at the same time, interviewing

large numbers of prospective migrants in home countries is more cost-effective.

The paper proceeds as follows. Section two presents data. Section three discusses the

variables used in the analysis. Section four describes the estimation strategy. Section five

presents and discusses empirical results, followed by conclusion.

II. DATA

Data for this study come from the “Life in Transition 2” survey (LITS-2), conducted by the

European Bank for Reconstruction and Development (EBRD) and the World Bank in autumn

2010. Twenty eight post-socialist economies of Central and Eastern Europe and Central Asia,

Mongolia, Turkey, as well as five Western European countries (France, Germany, Italy,

Sweden and the UK), participated in the survey. The nationally representative samples

consist of 1,000 respondents, aged 18 and above, per country (1,500 respondents in the case

of Russia, Ukraine, Uzbekistan, Serbia, Poland and the UK).

In each country, the households were selected according to a two-stage clustered stratified

sampling procedure. In the first stage, the frame of primary sampling units was established

using information on local electoral territorial units. In the second stage, a random walk

fieldwork procedure was used to select households within primary sampling units. Steves

(2011) provides the survey summary, including detailed information on survey design and

implementation methodology.

9 However, the population of the origin country is also a selected one, as it excludes people who have already

emigrated.

9

I exclude from our analysis the five Western European countries,10

as people there tend to

have lower emigration propensities than in Eastern Europe and Central Asia. Excluding

Western Europe allows concentrating on a set of Eastern European and Central Asian

countries which share similar historical, economic and institutional backgrounds. Many of

these countries witnessed important emigration following the break-down of the socialist

block and, as a group, they are known to have relatively low life satisfaction levels (Easterlin,

2009; Guriev and Zhuravskaya, 2009).

III. VARIABLES

1. Dependent variable: emigration intentions

I use two questions to create two dichotomous variables capturing individual likelihood of

migration. The first variable, emigration intentions, draws on the question “Do you intend to

move abroad in the next 12 months?” (possible answers ‘yes’ or ‘no’). The second variable,

willingness to migrate, draws on the question “Would you be willing to move abroad for

employment reasons?” (possible answers ‘yes’ or ‘no’).

A preferred dependent variable is emigration intentions, as the question used to construct it is

unambiguous, and the specified time frame (12 months) provides respondents an additional

focus. In contrast, the question used to construct the willingness to migrate variable is more

vague and can be interpreted in different ways: some respondents might think of their general

predisposition to migration, while others might think it is a hypothetical question and report

their likelihood of moving abroad is if a specific need to do so arises.

Out of all respondents, 27% said they would be willing to move abroad, and only 5% said

they intended to move abroad in the following 12 months. While one would expect

emigration intentions to be a subset of willingness to migrate, it does not have to be case:

26% of respondents intending to migrate provided a negative answer to the willingness

10

The final dataset consists of Albania, Armenia, Azerbaijan, Belarus, Bosnia, Bulgaria, Croatia, the Czech

Republic, Estonia, Georgia, Hungary, Kazakhstan, Kosovo, Kyrgyzstan, Latvia, Lithuania, former Yugoslavian

Republic of Macedonia, Moldova, Mongolia, Montenegro, Poland, Romania, Russia, Serbia, Slovakia,

Slovenia, Tajikistan, Turkey, Ukraine and Uzbekistan. Turkey and Mongolia – two countries which are

typically not associated with the post-socialist world – are also kept in the sample; the results do not change if

Turkey and Mongolia are excluded.

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question, i.e., said they would not be willing to migrate for employment purposes. This could

be because willingness to migrate refers specifically to work migration, while emigration

intentions captures, more broadly, all types of migration – work, family reunification, student

etc. It should also be noted that the two questions were asked in different sections of the

survey, which excludes the possibility of a cognitive bias, where the answers to one question

condition the answers to the other.

2. Main regressor: life satisfaction

Respondents’ life satisfaction is captured by the question, “All things considered, how

satisfied or dissatisfied are you with your life as a whole these days? Please answer on a scale

from 1 to 10, where 1 means completely dissatisfied and 10 means completely satisfied”.

This question, standard in interview surveys, provides a subjective measure of respondents’

well-being.11

However, as such, it may present several challenges for our analysis. First, the

rankings of life satisfaction are likely to be conditioned by country-wide cultural and

linguistic factors and may not be comparable across countries. To ensure that the results draw

on within- rather than between-country variation in life satisfaction, I include country-fixed

effects, which will capture all country-wide differences in life satisfaction.

The second concern is the question’s loosely defined time frame. A typical rationale for

including “these days” in the question is to discount any spontaneous deviations in subjective

well-being at the day of the interview and to make respondents take a longer term perspective

on their life satisfaction. However, it is not clear how a potential change in life satisfaction

due to the prospect of migration would fit into “these days” time frame. As mentioned before,

respondents who have taken a decision to move abroad may be temporarily off their

‘normal’, longer-term life satisfaction levels. To capture respondents’ underlying life

satisfaction, I will use instrumental variables - factors correlated with individual life

satisfaction but having no independent effect on emigration decision - to predict life

satisfaction.

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The Life in Transition-2 survey does not contain a question on happiness - another standard measure of

subjective well-being. This is unfortunate, as happiness could have been used to check the robustness of our

econometric results. Note, however, that there is no obvious reason to prefer one measure of wellbeing to the

other; where available, happiness and life satisfaction are highly correlated and tend to produce almost identical

results (Simpson, 2013).

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3. Control variables

To isolate the effect of life satisfaction on emigration intentions, one must control for

potential confounders – variables potentially affecting subjective well-being, emigration

decision, or both. Previous literature has shown that factors such as gender, age, family

composition, ethnic/linguistic minority status, education, income and employment status are

important determinants of both subjective well-being and emigration decision. Therefore, I

include dummy variables for gender (female), five age groups, five marital status groups

(single, married/ living with a partner, divorced/separated, widow), having children, three

education levels (primary, secondary, tertiary) and being employed. The minority status is

captured by the information on the respondent’s mother tongue – the linguistic minority

dummy is equal to 1 if it is different from the respondent’s country of residence state/official

language(s). Dummy variables for three types of settlement (rural, urban, metropolitan) are

also included.

Income is a crucial control variable potentially affecting both the decision to migrate and life

satisfaction. Unfortunately, the survey does not contain information on respondent or

household actual income.12

Therefore, I consider different proxies for household income.

First, I used information on household assets (car, secondary residence, bank account, debit

card, credit card, mobile phone, computer and internet access at home) to create a wealth

index using principal components. Second, I used information on where respondents thought

they were on a ten-step income ladder, where the first (tenth) step captures the poorest

(richest) 10% of the country. While this variable may suffer from subjectivity bias (there is

no guarantee that everyone imagines the ten-step income ladder in the same way), it is

important to recognise that a perception of own income, especially relative to one’s reference

group which can well be a country, may be an important determinant of both migration (Stark

and Taylor, 1991) and life satisfaction (Clark et al., 2008). Third, I include a variable

financial satisfaction, constructed from the question, “All things considered, I am satisfied

with my financial situation as a whole” (five possible answers ranging from “strongly

disagree” (1) to “strongly agree” (5)). Financial satisfaction has been shown to be a strong

predictor of life satisfaction, especially in poorer countries (Oishi et al., 1999), and, as a

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The survey contains information on household monthly expenditure on different goods (food, utilities,

transport, education, health, clothing and durable goods), as well as information on household monthly savings.

I use this information to create a total expenditure and savings adult equivalence variable. Closer inspection of

this variable revealed a ‘don’t know’/ non-response rate of 46% and it was decided not to use it because of the

huge loss in information this would cause.

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manifestation of individual income and/or wealth, could also matter for shaping emigration

intentions. The three proxies of income are positively, but less than perfectly,13

correlated,

and will be jointly included in our model.14

A particular challenge is presented by finding a proxy for migrant networks – a crucial driver

of emigration decision. Unfortunately, only those respondents who said they intended to

emigrate in the following 12 months were asked whether they had friends or relatives in the

place they planned to move to. This information cannot be used to construct a networks

dummy, as it would be conditioned by the dependent variable. Instead, I use all the

information which can indicate indirectly that a respondent might have family or friends

connections abroad. First, in the section on the impacts of the global economic crisis, the

respondents were asked whether, in the two years prior to the interview, they had experienced

a fall in remittances and a household member had returned from working abroad. Second, the

respondents who said they had worked in the previous 12 months were asked about where the

work was primarily done, with ‘abroad’ being one of the possible answers. I construct a

migrant networks dummy which is equal to 1 if at least one of the following is true: 1)

respondent’s household experienced a crisis-related fall in remittances; 2) a household

member returned from abroad due to the crisis; 3) the respondent worked abroad in the last

12 months, and 0 otherwise. With the average of 0.15, this variable represents a lower bound

of the intensity of migrant networks.

I also want to control for the respondents’ health status, as previous literature suggests that

good health has a strong positive association with subjective well-being (MacKerron, 2012;

Diener and Chan, 2011); in addition, it could be argued that good health matters for

migration. The respondents were asked, “How would you assess your health?” Possible

answers “very good” and “good” are coded into a good health dummy, “very bad” and “bad”

into a bad health dummy, with “medium” health remaining the reference category.

Finally, to control for all possible country-wide influences on emigration decision and life

satisfaction, I include country dummies. This will also ensure that the analysis captures

within-country individual-level relationships between the variables of interest.

13

The correlation between wealth index and perceived income decile is equal to 0.30, between wealth index and

financial satisfaction, 0.14, and between perceived income decile and financial satisfaction, 0.44. 14

As a robustness check, to control for possible non-linearities in the emigration intentions-income relationship,

I have replaced continuous perceived relative income and financial satisfaction variables with a set of dummies

capturing their different values. This did not affect the results of the life satisfaction variable.

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IV. IDENTIFICATION OF CAUSAL EFFECTS

The principal objective of this study is to establish causal effects of life satisfaction on

emigration intentions. To identify causality, I use instrumental variable analysis. The method

relies on the availability of instruments which are relevant (highly correlated with the life

satisfaction) and exogenous (affecting emigration intentions only through life satisfaction).

Ideally, I also want the instruments to predict the long-term, underlying component of one’s

life satisfaction. Arguably, such long-term trends in subjective well-being can be traced back

to respondents’ childhood environments. As the LITS-2 survey contains some information on

respondents’ parents and other family members, I explore this information to construct the

instruments.

The first instrument is parental years of education. I argue that, other things equal, more

educated parents are more responsive to the emotional needs of their children, which, in turn,

has a positive long-term effect on the subjective well-being of the children. Therefore, I

expect parental schooling to be positively correlated with respondents’ current life

satisfaction. In practical terms, respondents were asked about the level of schooling

(completed years of education) of their mother and father. I choose father’s years of

education, as it has higher predictive power in the first stage regression than mother’s

schooling.15

The second instrument captures the fact that a respondent’s family member was a victim of

World War II (WWII) and is based on the question “Were you, your parents or any of your

grandparents physically injured or killed during the Second World War?” Frey (2012)

discusses various channels through which wars can affect the well-being of soldiers, their

family members and other civilians. On the one hand, one would naturally expect wars to

reduce happiness; there is ample evidence that soldiers returning from wars are more likely to

suffer from post-traumatic stress disorder, depression, and alcohol and drug abuse, as well as

to commit crime, violence and suicide.16

However, Frey (2012) also argues that wars can

increase happiness of soldiers and their family members. In retrospect, soldiers and victims of

15

The F test of excluded instrument for father’s schooling is 24.22 and for mother’s schooling 18.50. If both are

jointly included as instruments, mother’s schooling becomes statistically insignificant, while father’s remains

highly significant, and the F test statistic reduces to 12.13. 16

See Frey (2012) for an overview of this literature.

14

war may reconsider their war experience in a positive light because they are happy to have

survived (the ‘afterglow’ effect). Fighting in wars may result in feelings of shared purpose,

solidarity, trust, friendship and national pride, all of which could enhance happiness. Many

wars, including WWII, were proclaimed as serving a desirable goal, and family members

may not mourn and actually be proud that someone in their family died as a ‘martyr’ or was

fighting for a ‘good’ or ‘holy’ cause. It is, thus, possible that people fighting for, and affected

by, such ‘ennobling’ wars “convert the costs of war into psychic benefits” (Frey, 2012,

p.369).

In soviet times, WWII played, and in some countries continues to play, a major role in

political discourse and ideology, and the formation of national identities. The victims of

WWII,17

surviving soldiers and civilians who suffered during the war were officially

venerated as the heroes of the ‘Great Patriotic War’. WWII occupied a prominent place in the

minds of ordinary citizens: it was a major life event for people who were engaged in it, and

the war memories were – and continue to be – passed through generations. Thus, the children

and grandchildren of people who were killed, injured or suffered during WWII had all the

reasons to be proud about the heroic past of their family members. They enjoyed different

forms of societal and institutional respect and attention, and were exposed to (often

‘ennobled’) family stories of war hardships, survivals and deaths. All these factors may have

had a positive effect on the psychological development and well-being of the WWII victims’

descendants, and one can expect that, today, these people report higher levels of subjective

well-being relative to people in similar circumstances but without the presence of WWII

‘heroes’ in the family.18

To test whether the instrument are relevant, the F test of the joint significance of excluded

instruments will be conducted after the first stage regression. A value higher than a

commonly accepted threshold of 10 would indicate that instruments are relevant (sufficiently

strong predictors of life satisfaction). In addition, it is important that each instrument is

individually statistically significant in the first stage equation.

Proving instrument exogenity is the most challenging part of the instrumental variable

analysis. Strictly speaking, the exogeneity of instruments cannot be technically proven, and

17

For the Soviet Union alone, the WWII death toll exceeded 20 million people (Ellmann and Maksudov, 1994). 18

Frey (2011, 2012) also discusses the phenomenon of ‘combat flow’ – energising and addictive combat

experience which may increase soldiers’ subjective well-being during the war. It is, however, unlikely that this

particular happiness gain would persist after the war or would be transmitted across generations.

15

one has to believe that instrument(s) affect the outcome of interest only through the

endogenous regressor. The instrument over-identification test, which is widely used to test

instrument exogeneity, rests on the assumption that at least one instrument is valid.

In this regard, the WWII instrument would seem a more convincing option: apart from the

proposed life satisfaction channel, it is difficult to come up with explanations of why the fact

that someone was killed or injured during WWII would affect emigration intentions of his/her

descendants more than sixty years later, especially if one controls for the respondent’s

standard socio-demographic characteristics, such as income, education etc. The exogeneity of

the parental education instrument is more questionable. More educated parents might have

explicit strategies of preparing their children for emigration, such as investing in their foreign

language skills. Also, children of more educated parents might have more experience of

travelling and higher (unobserved) innate ability, both of which might affect emigration

propensity. Another indirect channel is related to the fact that, during communist times,

highly educated people were more likely to be members of the Communist party. If the

political capital was transferred after the regime change, the descendants of the party

members might currently be benefitting from wider resources which would help either to

emigrate or find employment at home. The communist party channel may also be an issue for

the WWII instrument, if the party members were more likely to fight in the war or if the party

explicitly targeted the WWII veterans to become its members.

Given these concerns, both first and second stage regressions of the instrumental variable

analysis will control for the parental and familial links to the Communist party (the

respondents were asked whether they themselves, their parents or other family members were

members of the party before the dissolution of the socialist block). The standard

overidentification test of instrument exogeneity will then be performed; a statistically

insignificant test statistics would support the hypothesis that the instruments are exogenous.

Finally, to check whether endogeneity is present in the model in the first place, the regressor

endogeneity test will be conducted. It tests whether residuals from the first-stage regression

are correlated with the residuals of the structural model. A coefficient statistically different

from zero would indicate that endogeneity is present and the instrumental variable estimation

should be used; if the correlation is insignificant, the endogenous regressor can be treated as

exogenous and a naïve model is sufficient.

16

Despite the binary nature of the dependent variable (intentions to migrate/ willingness to

work abroad), the instrumental variable models will be estimated with the two stage least

squares and the corresponding naïve models (which do not account for the endogeneity of life

satisfaction) with OLS. The linear IV model is preferred to the IV probit estimation, as the

latter does not produce all the instrument validity tests19

and the results of the linear model

are easier to interpret. As a robustness check, the naïve and IV probit models have also been

estimated; the probit results are qualitatively similar to the results of the corresponding linear

models and are available on request.

Individuals older than 64 are excluded from the analysis, as, generally, they have very low

propensities to migrate. All estimations use robust standard errors, clustered at the locality

(village/ town/ city) level, and apply within-country population weights available from the

survey dataset.

V. RESULTS

Column 1 of Table 1 reports the results of a naïve linear probability model explaining the

intentions to move abroad with life satisfaction, socio-demographic controls and country-

fixed effects. The regressor of interest, life satisfaction, is a negative, significant at 10% and,

in terms of magnitude, very modest covariate of emigration intentions. Keeping other factors

constant, one step on the 1 to 10 life satisfaction scale is associated with a 0.0018 decrease in

the dependent variable. A naïve regression, thus, would suggest that it is the unhappier people

who are more likely to report intentions to migrate, although the difference in the probability

of reporting emigration intentions between people who are the least and the most satisfied

with their lives is only 1.8 percentage points. This relationship is represented by a relatively

flat dashed line on the left panel of Figure 1.

A different picture emerges if the squared term of the life satisfaction variable is also

included in the regression (column 2). Now both life satisfaction and its square are strongly

significant. The negative coefficient of the former and the positive coefficient of the latter

imply a U-shaped relationship between life satisfaction and the probability of reporting

19

For example, the ivprobit command in Stata does not include the instrument overidentification test. See also

Ivlevs and King (2012) for a discussion of testing instrument exogeneity in non-linear models.

17

emigration intentions, with the turning point corresponding to 6.20 units of life satisfaction.

For relatively low levels of life satisfaction (lower than 6.20/10), the likelihood of reporting

intentions to migrate falls with the reported life satisfaction; for relatively high levels of life

satisfaction (higher than 6.20/10), the likelihood of reporting intentions to migrate increases

with reported life satisfaction. This said, the substantive significance of the life satisfaction

variables is low. As can be assessed from the solid line on the left panel of Figure 1, a

movement on the life satisfaction scale from 1 to 6.20 (the inflection point) is associated with

an approximately 4 percentage points lower probability of reporting emigration intentions,

while a movement from 6.20 to 10 is associated with a 2.2 percentage points higher

probability of reporting emigration intentions.

Similar results are obtained if the dependent variable is willingness to migrate. When the life

satisfaction variable is included without its square (column 3 of Table 1), its coefficient is

negative but statistically insignificant. A joint inclusion of the two life satisfaction variables

(column 4) again suggests a statistically significant U-shaped relationship between

willingness to migrate and the main regressor, with the implied turning point occurring when

the value of the life satisfaction variable is equal to 5.61 (which is close to the mean value of

the life satisfaction variable, 5.37). In terms of the actual impact on the dependent variable, a

movement from 1 to 5.61 (5.61 to 10) in life satisfaction is associated with a decrease

(increase) in the probability of reporting willingness to migrate of 5.5 (4.1) percentage points

(right panel of Figure 1). Thus, the naïve regression results suggests that, as life satisfaction

increases, both intentions and willingness to migrate first decrease and then increase with it,

and the substantive significance of the life satisfaction variable is low.

Before proceeding to the instrumental variable results, I would like to comment on the

coefficients of the socio-demographic controls. They are largely in line with what one would

expect: women, older people, those satisfied with their financial situation are less likely

express desire to migrate, while the single and those with migrant networks are more likely to

do so. Wealth index has a positive association with both dependent variables, which could be

explained by the necessity to have initial capital to cover migration costs. Linguistic

minorities also appear more prone to migration, which can be explained by the various types

of disadvantages that minorities face in source countries and is consistent with previous

literature (see, e.g., Ivlevs, 2013). Higher levels of education, living in urban areas and

having familial Communist party connections before fall of the socialist block are associated

18

with a higher willingness (but not intentions) to migrate. These respondent groups could be

characterised by particular aspirations for superior quality of life (amenities, health care,

education, well-functioning institutions), as well as access to specific information or

networks which may facilitate migration.

Table 1. Life satisfaction and intentions/willingness to move abroad, linear probability

model (OLS) results.

Intentions to move

abroad (0/1)

Willingness to work

abroad (0/1)

[1] [2] [3] [4]

Life satisfaction (1 – low, … 10 – high) -0.0018* -0.0191*** -0.0010 -0.0277***

Life satisfaction squared/100 - 0.154*** - 0.247***

Female -0.028*** -0.027*** -0.065*** -0.064***

Age group

18-24 0.019*** 0.019*** 0.106*** 0.106***

25-34 0.011** 0.011** 0.045*** 0.045***

35-44 Ref. Ref. Ref. Ref.

45-54 -0.016*** -0.016*** -0.062*** -0.062***

55-64 -0.021*** -0.021*** -0.137*** -0.136***

Marital status

Single 0.034*** 0.034*** 0.104*** 0.103***

Married/ relationship Ref. Ref. Ref. Ref.

Divorced/ separated 0.020*** 0.019*** 0.049*** 0.048***

Widow 0.007 0.006 -0.016 -0.018*

Has children -0.003 -0.004 -0.013* -0.013*

Linguistic minority 0.022*** 0.022*** 0.033** 0.032**

Education

Primary -0.003 -0.004 -0.031*** -0.031***

Secondary Ref. Ref. Ref. Ref.

Tertiary -0.001 -0.001 0.038*** 0.038***

Wealth index 0.005*** 0.005*** 0.023*** 0.023***

Perceived income decile (1 – low.. 10 – high) -0.001 -0.000 -0.009*** -0.008***

Satisfied with financial situation (1 – low.. 5 – high) -0.003* -0.003* -0.028*** -0.027***

Employed -0.002 -0.001 0.029*** 0.030***

Type of settlement

Rural -0.002 -0.002 -0.033*** -0.032***

Urban Ref. Ref. Ref. Ref.

Metropolitan -0.007 -0.008 0.003 0.003

Health

Bad -0.006 -0.009* 0.006 0.002

Medium Ref. Ref. Ref. Ref.

Good 0.001 0.001 0.006 0.006

Migrant networks 0.032*** 0.031*** 0.031*** 0.031***

Family member in communist party before 1991 0.001 0.001 0.026*** 0.027***

Country fixed effects Yes Yes Yes Yes

Observations 26358 26358 26358 26358

R2 0.064 0.065 0.114 0.114

F tests (p > F) 0.000 0.000 0.000 0.000

Notes: *** p<0.01, ** p<0.05, * p<0.1. Robust standard errors, clustered at locality level, used to calculate

regressors’ statistical significance.

19

Figure 1. Life satisfaction and predicted intentions/ willingness to migrate.

Note: In reference to Table 1, the dashed line represents specifications 1 and 3, and the solid line represents

specifications 2 and 4.

Next, I turn to the results of the instrumental variable estimation (Table 2). First, the regressor

endogeneity test strongly rejects the hypothesis that the error terms from the first-stage

equation are uncorrelated with those of the structural model, meaning that endogeneity is

present and instrumental variable estimation should be used. The first-stage results (column

1) suggest that the instruments are individually statistically significant and have expected

signs: other things equal, both father’s schooling and having a relative who was either killed

or injured in the WWII are positively and significantly correlated with life satisfaction. The

relevance of the instruments is confirmed by the F test value of 12.52, which is higher than

the threshold value of 10. The insignificant statistic of the overidentification test (0.51 for the

intentions and 0.91 for the willingness model) suggests that the instruments are exogenous

i.e., they influence intentions to migrate only through life satisfaction. The tests, thus, support

the appropriateness of the chosen instruments.

The instrumental variable estimation (columns 2 and 3) shows that life satisfaction exerts a

positive and statistically significant effect on the probability of reporting intentions and

willingness to migrate. Moving up one step on the life satisfaction scale increases the

probability of reporting intentions and willingness to migrate by 14.6 and 31.7 percentage

points, respectively. These relatively large, in terms of magnitude, effects counter the

correlational evidence of this and other studies, which point at a negative association between

.05

.06

.07

.08

.09

Inte

ntion

s to m

igra

te, pre

d. pro

bab

ility

1 2 3 4 5 6 7 8 9 10

Life satisfaction

linear relationship quadratic relationship

Life satisfaction and intentions to migrate

.27

.28

.29

.3.3

1.3

2.3

3

Will

ing

ne

ss to m

igra

te, pre

d. pro

bab

ility

1 2 3 4 5 6 7 8 9 10

Life satisfaction

quadratic relationshiplinear relationship

Life satisfaction and willingness to migrate

20

Table 2. Life satisfaction and intentions/willingness to move abroad, 2SLS results.

Instrumental variable probit estimation

First stage

Dependent

variable:

Life satisfaction

Second stage:

Intentions to move

abroad (0/1)

Second stage:

Willingness

to work

abroad (0/1)

[1] [2] [3]

Life satisfaction (1 – low, … 10 – high) - 0.146*** 0.317***

Female 0.062** -0.042*** -0.097***

Age group

18-24 0.259*** -0.021 0.021

25-34 0.042 0.003 0.029*

35-44 Ref. Ref. Ref.

45-54 0.059 -0.022*** -0.070***

55-64 0.235*** -0.053*** -0.215***

Marital status

Single -0.047 0.038*** 0.110***

Married/ relationship Ref. Ref. Ref.

Divorced/ separated -0.251*** 0.055*** 0.119***

Widow -0.226*** 0.044*** 0.058**

Has children 0.075** -0.013* -0.034**

Linguistic minority -0.126** 0.047*** 0.082***

Education

Primary -0.104*** 0.018* 0.014

Secondary Ref. Ref. Ref.

Tertiary 0.163*** -0.028*** -0.022

Wealth index 0.132*** -0.015** -0.022*

Perceived income decile (1 – low.. 10 – high) 0.348*** -0.052*** -0.119***

Satisf. with financ. situation (1 – low.. 5 – high) 0.403*** -0.063*** -0.158***

Employed 0.018 -0.009 0.024*

Type of settlement

Rural 0.005 -0.001 -0.035*

Urban Ref. Ref. Ref.

Metropolitan 0.044 -0.010 -0.017

Health

Bad -0.313*** 0.045*** 0.112***

Medium Ref. Ref. Ref.

Good 0.234*** -0.033*** -0.071***

Migrant networks -0.078* 0.046*** 0.050***

Family member in communist party before 1991 -0.094*** 0.011 0.057***

Country fixed effects Yes Yes Yes

Instruments:

Father’s years of education 0.019***

Family member killed or injured in WWII 0.088***

Regressor endogeneity test (p-value) 0.000 0.000

F test of excluded instruments 12.516***

Overidentification test (Hansen J stat p-value) 0.510 0.910

Observations 20605 20605 20605

F tests (p > F) 0.000 0.000 0.000

Notes: *** p<0.01, ** p<0.05, * p<0.1. Robust standard errors used to calculate regressors’ statistical

significance.

21

the subjective well-being and desire to emigrate.20

This discrepancy could be explained by

reverse causality, whereby preparation for migration temporarily reduces subjective well-

being. It is also possible that the bias is due to unobserved variables: for example, sudden

problems with one’s job or a deterioration of relationships in one’s family – factors which are

generally not taken into account in naïve cross-sectional studies – would both reduce

subjective well-being and increase the desire to migrate.

Dealing with missing values for parental education

A closer look at Tables 1 and 2 reveals that the sample size in the instrumental variable

estimations is about four fifths of the sample size for naïve regressions. This is because 23

percent of the respondents did not provide an answer to the question on father’s education

and therefore had to be excluded from the instrumental variable estimations. Such a high non-

response rate could bias the instrumental variable results. In this subsection, I comment on

several checks conducted to find out whether such bias is present.

First, I check whether selection bias is present in the naïve estimations which exclude

respondents who did not provide an answer on their father’s education. Using a Heckman

correction model, where the non-response on mother’s education serves as identification

variable for providing an answer on father’s education, no evidence is found that selection

bias is present; in other words, estimating the model on a restricted sample does not produce

biased estimates.21

This is confirmed by virtually the same results of the estimations which

exclude respondents who did not report the education of their father and the corresponding

full-sample estimations presented in Table 1.

Second, I recode the years of father’s education variable into a binary variable father with

higher education, which take the value of 1 if father had at least 15 years of schooling (which

20

I have also checked whether the instrumental variable estimation supports a curvilinear relationship between

life satisfaction and intentions/ willingness to migrate. The F test of excluded instruments supports the

hypothesis that the instruments are valid (which is not surprising given that the two endogenous regressors are

related); however, the overidentification test cannot be performed as the two endogenous regressors (life

satisfaction and its square) are now predicted by two instruments. The IV results are statistically insignificant

and do not support a curvilinear relationship between life satisfaction and intentions/ willingness to migrate.

21 The Wald test of independent equations rejects the hypothesis that a sample selection bias is present – the Rho

coefficient is statistically insignificant both in the model explaining intentions to move (p=0.31) and in the

model explaining willingness to move (p=0.48). For a formal treatment of Heckman correction model, see, e.g.,

Wooldridge (2010).

22

would correspond to completed university-level education) and 0 if father had less than 15

years of schooling (primary, secondary and secondary vocational education) or when the

information of father’s education is missing; effectively, I am assuming that the education

level of the non-respondents’ fathers is less than completed university-level education.22

Replacing father’s years of education with father’s higher education dummy in the

instrumental variable estimations suggests that moving up one unit on the 1 to 10 life

satisfaction scale results in a 15.2 percentage points higher probability of reporting intentions

to migrate and a 30 percentage points higher probability of reporting willingness to migrate.

These results are in line with those obtained using a restricted sample.

VI. CONCLUSION

This paper has studied how people select into migration on the basis of subjective well-being

in 30 countries of Eastern Europe and Central Asia. It aimed explicitly at determining the

causal effects of life satisfaction on the desire to emigrate – a question of particular

importance for an increasing number of policymakers across the world, who aim at increasing

people’s subjective well-being. The analysis of causal effects, where life satisfaction was

instrumented with parental education and the fact that someone in the family was injured or

killed during WWII, revealed that higher levels of life satisfaction contribute to a higher

probability of reporting emigration intentions and willingness to migrate.

This result should be disturbing news for policymakers in migrant-sending countries. Recent

evidence suggests that higher levels of subjective well-being make people more productive,

creative, healthy and sociable. The outflow of happy people – ‘happiness drain’ – would

deprive migrants-sending countries of these positive externalities of happiness. More

generally, the positive effect of subjective well-being on the desire to emigrate puts under

question the worthwhileness of happiness-enhancing initiatives: why increase people’s

happiness if happier people are more likely to migrate? On the other hand, the positive

selection of migrants on the basis of subjective well-being should be good news in migrant-

22

Regressing the non-responses to the parental education question on the socio-demographic controls in a

binary probit model reveals that respondents with lower levels of education are particularly likely to provide a

non-response. Given that parents’ and children’s education levels tend to be highly correlated (intergenerational

transmission of education), one may expect the non-responses to be positively correlated with lower levels of

parental education.

23

receiving countries. Happier, hence more productive, healthy and sociable migrants, are

likely to put less pressure on the welfare state and integrate easier into host societies,

compared to the situation where migrants were negatively selected on subjective well-being.

Several notes of caution, however, have to be made. First, the desire to emigrate does not

always need to translate into an actual move.23

‘Dreaming’ about emigration – expressing a

desire to migrate which is never followed by actual migration – may well be a characteristic

of a happy person. Second, even if it is the happiest people who are more prone to emigration

in sending countries, their level of subjective well-being may still remain below that of

people in the host society. Under such circumstances, the benefits of high subjective well-

being in terms of greater productivity, health and sociability may not fully materialise. Third,

it is possible that migration experience makes people less happy.24

If this is the case, the

happiness capital that migrants bring with them into the host society, and the associated long-

term benefits, may erode.

Taken together, a well-informed policy advice will need to draw on the answers to several

questions: 1) how actual migrants select on subjective well-being in sending countries; 2)

whether experience of living in a host country makes people more or less happy; and 3)

whether, after a period of adaptation in the host country, migrants are more or less happy than

natives. While this and other studies have already shed some light on these questions, no

study has yet addressed all three questions jointly in relation to the same migrants. An

ambitious data collection effort, which, for a particular migration episode, would involve

both origin and destination countries and follow migrants over time, is key in providing a

complete picture on the migration - subjective well-being nexus.

23

The existing evidence on the link between willingness/ intentions to migrate and the actual move is available

for the UK (Boheim and Taylor, 2002), the Netherlands (van Dalen and Henkens, 2013) and Mexico (Creighton,

2013). It is unclear how strong this link is in the post-socialist countries of Eastern Europe and Central Asia. 24

Bartram (2010) summarises various channels through which migration experience might reduce one’s

subjective well-being. They include: a fall in relative position when migrants change their reference groups from

the origin to destination country populations; exposure to new consumption patterns and higher levels of

affluence which change migrants’ aspirations but do not necessarily lead to their fulfilment; developing a mental

illness, such as depression, as rates of mental illness are higher in wealthier than poorer countries. These factors

are related to the migrants’ adaptation to the new environment in the host country and are not necessarily taken

into account when the decision to migrate is being made.

24

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27

Appendix.

Summary statistics.

Obs. Mean St.dev. Min Max

Intentions to emigrate 27862 0.054 0.226 0 1

Willingness to work abroad 27862 0.272 0.445 0 1

Life satisfaction (1 – low, … 10 – high) 27856 5.369 2.039 1 10

Female 27821 0.604 0.489 0 1

Age group

18-24 27862 0.152 0.359 0 1

25-34 27862 0.243 0.429 0 1

35-44 27862 0.221 0.415 0 1

45-54 27862 0.206 0.404 0 1

55-64 27862 0.178 0.382 0 1

Marital status

Single 27675 0.216 0.412 0 1

Married/ relationship 27675 0.637 0.481 0 1

Divorced/ separated 27675 0.089 0.285 0 1

Widow 27675 0.058 0.233 0 1

Has children 27862 0.442 0.497 0 1

Linguistic minority 27862 0.131 0.337 0 1

Education

Primary 27860 0.256 0.437 0 1

Secondary 27860 0.538 0.499 0 1

Tertiary 27860 0.206 0.405 0 1

Wealth index 27862 -0.071 1.678 -2.711 3.328

Perceived income decile (1 – low.. 10 – high) 27400 4.437 1.658 1 10

Satisf. with financ. situation (1 – low.. 5 – high) 27064 2.723 1.110 1 5

Employed 27862 0.558 0.497 0 1

Type of settlement

Rural 27862 0.414 0.493 0 1

Urban 27862 0.469 0.499 0 1

Metropolitan 27862 0.116 0.321 0 1

Health

Bad 27722 0.091 0.287 0 1

Medium 27722 0.330 0.470 0 1

Good 27722 0.580 0.494 0 1

Migrant networks 27862 0.154 0.361 0 1

Father’s years of education 21509 9.705 4.097 0 50

Family member killed or injured in WWII 27862 0.246 0.431 0 1

Family member in communist party before 1991 27862 0.365 0.481 0 1

28

Recent UWE Economics Papers

See http://www1.uwe.ac.uk/bl/bbs/bbsresearch/economics/economicspapers.aspx for a full list

2014

1402 Happy moves? Assessing the impact of subjective well-being on the emigration decision

Artjoms Ivlevs

1401 Communist party membership and bribe paying in transitional economies

Timothy Hinks and Artjoms Ivlevs

2013

1315 Global economic crisis and corruption experience: Evidence from transition economies

Artjoms Ivlevs and Timothy Hinks

1314 A two-state Markov-switching distinctive conditional variance application for tanker freight returns

Wessam Abouarghoub, Iris Biefang-Frisancho Mariscal and Peter Howells

1313 Measuring the level of risk exposure in tanker shipping freight markets

Wessam Abouarghoub and Iris Biefang-Frisancho Mariscal

1312 Modelling the sectoral allocation of labour in open economy models

Laura Povoledo

1311 The US Fed and the Bank of England: ownership, structure and ‘independence’

Peter Howells

1310 Cross-hauling and regional input-output tables: the case of the province of Hubei, China

Anthony T. Flegg, Yongming Huang and Timo Tohmo

1309 Temporary employment, job satisfaction and subjective well-being

Chris Dawson and Michail Veliziotis

1308 Risk taking and monetary policy before the crisis: the case of Germany

Iris Biefang-Frisancho Mariscal

1307 What determines students’ choices of elective modules?

Mary R Hedges, Gail A Pacheco and Don J Webber

1306 How should economics curricula be evaluated?

Andrew Mearman

1305 Temporary employment and wellbeing: Selection or causal?

Chris Dawson, Don J Webber and Ben Hopkins

1304 Trade unions and unpaid overtime in Britain

Michail Veliziotis

1303 Why do students study economics?

Andrew Mearman, Aspasia Papa and Don J. Webber

1302 Estimating regional input coefficients and multipliers: The use of the FLQ is not a gamble

Anthony T. Flegg and Timo Tohmo

1301 Liquidity and credit risks in the UK’s financial crisis: How QE changed the relationship

Woon Wong, Iris Biefang-Frisancho Mariscal, Wanru Yao and Peter Howells

29

2012

1221 The impact of the quality of the work environment on employees’ intention to quit

Ray Markey, Katherine Ravenswood and Don J. Webber

1220 The changing influence of culture on job satisfaction across Europe: 1981-2008

Gail Pacheco, De Wet van der Westhuizen and Don J. Webber

1219 Understanding student attendance in Business Schools: an exploratory study

Andrew Mearman, Don J. Webber, Artjoms Ivļevs, Tanzila Rahman & Gail Pacheco

1218 What is a manufacturing job?

Felix Ritchie, Andrew D. Thomas and Richard Welpton

1217 Rethinking economics: Logical gaps – empirical to the real world

Stuart Birks

1216 Rethinking economics: Logical gaps – theory to empirical

Stuart Birks

1215 Rethinking economics: Economics as a toolkit

Stuart Birks

1214 Rethinking economics: Downs with traction

Stuart Birks

1213 Rethinking economics: theory as rhetoric

Stuart Birks

1212 An economics angle on the law

Stuart Birks

1211 Temporary versus permanent employment: Does health matter?

Gail Pacheco, Dominic Page and Don J. Webber

1210 Issues in the measurement of low pay: 2010

Suzanne Fry and Felix Ritchie

1209 Output-based disclosure control for regressions

Felix Ritchie

1208 Sample selection and bribing behaviour

Timothy Hinks and Artjoms Ivļevs

1207 Internet shopping and Internet banking in sequence

Athanasios G. Patsiotis, Tim Hughes and Don J. Webber

1206 Mental and physical health: Reconceptualising the relationship with employment propensity

Gail Pacheco, Dom Page and Don J. Webber

1205 Using student evaluations to improve individual and department teaching qualities

Mary R. Hedges and Don J. Webber

1204 The effects of the 2004 Minority Education Reform on pupils’ performance in Latvia

Artjoms Ivļevs and Roswitha M. King

1203 Pluralist economics curricula: Do they work and how would we know?

Andrew Mearman